<![CDATA[Inferential statistics enables drawing conclusions about a population from sample data. Hypothesis testing involves formulating a null hypothesis (H₀) and an alternative hypothesis (H₁). A p-value indicates the probability of obtaining results at least as extreme as those observed, assuming H₀ is true. If the p-value is less than the predetermined significance level (α), commonly set at 0.05, H₀ is rejected in favor of H₁, suggesting statistical significance. Tests can be one-tailed or two-tailed, depending on the research question's directionality. Type I errors (false positives) and Type II errors (false negatives) are risks in hypothesis testing. Controlling these errors involves careful selection of α and consideration of the test's power, which is the probability of correctly rejecting a false null hypothesis. In studies involving multiple comparisons, adjustments such as the Bonferroni correction and the Holm–Bonferroni method are employed to control the family-wise error rate, thereby reducing the likelihood of Type I errors across multiple tests. These techniques adjust the significance thresholds to maintain the overall error rate within acceptable bounds.]]>
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